With the development of computer graphics, virtual reality technology and augmented reality technology, three-dimensional models are more widely used. The three-dimensional model requires to be semantically parsed, that is, it needs to know which semantic category each component in the three-dimensional models is attributable to, where each semantic category is a structural category in the three-dimensional models. For instance, it needs to know components in a three-dimensional vehicle model are attributable to a semantic category for doors or wheel, and so on.
In the prior art, a projection-based three-dimensional model semantic segmentation method is used, where the method proposes a novel Hausdorff distance and matching projected binary images of three-dimensional model based on the novel Hausdorff distance, and the distance takes an internal hole structure of a two-dimensional figures into consideration, which may better sense topological changes and may be applied to piecewise-linearly warped model projections to compensate for scale transformation and view discrepancies.
In the prior art, the projection-based three-dimensional model semantic segmentation method can handle an imperfect three-dimensional model, but cannot analyze a three-dimensional model having an “inner-external” structure, for instance, for internal components in the three-dimensional vehicle model, such as a steering wheel, seats, etc., the method cannot semantically parse and process the three-dimensional model. Therefore, the method in the prior art cannot parse and process the three-dimensional model having the “internal-external” structure with respect to its semantic category, and thus parsing and identification effects are poor.